Effect of [Cu/In] ratio on the optical properties of CuInS2 thin films prepared by chemical spray pyrolysis on glass slides at 300oC was studied. The optical characteristics of the prepared thin films have been investigated using UV-VIS spectrophotometer in the wavelength range (300-1100 nm). The films have a direct allow electronic transition with optical energy gap (Eg) decreased from 1.51 eV to 1.30 eV with increasing of [Cu/In] ratio and as well as we notice that films have different behavior when annealed the films in the temperature 100oC (1h,2h), 200oC (1h,2h) for [Cu/In]=1.4 . Also the extinction coefficient (k), refractive index (n) and the real and imaginary dielectric constants (ε1, ε2) have been investigated

     With the increasing use of antibiotics around the world, the study and appreciation of antibiotics has become essential. An antibiotic formulation may include one or added active ingredients depending on the type and method of manufacturing the antibiotic. Antibiotics can only combat diseases of bacterial origin. As for viral diseases such as the common cold and influenza, antibiotics will not be able to combat them. The objective of this review is to digest the literature related to estimation of antibiotics and to show the methods that have been used in the estimation of the antibiotics (amoxicillin, ampicillin, cephalothin, carbenicillin, and cefotaxime) in medicinal preparations and a biological fluid for example blood

This work, deals with Kumaraswamy distribution. Kumaraswamy (1976, 1978) showed well known probability distribution functions such as the normal, beta and log-normal but in (1980) Kumaraswamy developed a more general probability density function for double bounded random processes, which is known as Kumaraswamy’s distribution. Classical maximum likelihood and Bayes methods estimator are used to estimate the unknown shape parameter (b). Reliability function are obtained using symmetric loss functions by using three types of informative priors two single priors and one double prior. In addition, a comparison is made for the performance of these estimators with respect to the numerical solution which are found using expansion method. The

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.

Studied red beetle life on each of the yen and wheat durum wheat, barley, corn, rice, chickpeas, ground peanuts and beans in Living situation constant temperature and relative humidity of 65% for a period of 66 days was the life cycle of the insect different from one substance to another ....

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others